Detection of Archaeological Surface Ceramics Using Deep Learning Image-Based Methods and Very High-Resolution UAV Imageries

Athos Agapiou, Athanasios Vionis and Giorgos Papantoniou
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Athos Agapiou: Earth Observation Cultural Heritage Research Lab, Department of Civil Engineering and Geomatics, Faculty of Engineering and Technology, Cyprus University of Technology, Limassol 3036, Cyprus
Athanasios Vionis: Archaeological Research Unit, University of Cyprus, Nicosia 1678, Cyprus
Giorgos Papantoniou: Department of Classics, School of Histories and Humanities, Trinity College Dublin, The University of Dublin, D02 PN40 Dublin, Ireland

Land, 2021, vol. 10, issue 12, 1-16

Abstract: Mapping surface ceramics through systematic pedestrian archaeological survey is considered a consistent method to recover the cultural biography of sites within a micro-region. Archaeologists nowadays conduct surface survey equipped with navigation devices counting, documenting, and collecting surface archaeological potsherds within a set of plotted grids. Recent advancements in unmanned aerial vehicles (UAVs) and image processing analysis can be utilised to support such surface archaeological investigations. In this study, we have implemented two different artificial intelligence image processing methods over two areas of interest near the present-day village of Kophinou in Cyprus, in the Xeros River valley. We have applied a random forest classifier through the Google Earth Engine big data cloud platform and a Single Shot Detector neural network in the ArcGIS Pro environment. For the first case study, the detection was based on red–green–blue (RGB) high-resolution orthophotos. In contrast, a multispectral camera covering both the visible and the near-infrared parts of the spectrum was used in the second area of investigation. The overall results indicate that such an approach can be used in the future as part of ongoing archaeological pedestrian surveys to detect scattered potsherds in areas of archaeological interest, even if pottery shares a very high spectral similarity with the surface.

Keywords: potsherds; detection; pedestrian survey; remote sensing archaeology; single shot detector; artificial intelligence; random forest; Google Earth Engine; Cyprus (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
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